Grafana MCP: Production AI Observability on AWS
Last updated 9/2026
Created by Opeyemi Onikute
MP4 | Video: h264, 3840×2160 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 32 Lectures ( 2h 3m ) | Size: 4.5 GB
Deploy Grafana MCP with Docker, Terraform, EKS, and ECS. Query from Cursor, Claude Code, Devin, and OpenCode
What you’ll learn
Understand the Model Context Protocol end-to-end
Master how to run and operate the Grafana MCP server
Deploy and harden Grafana MCP in production
Build a production observability stack on AWS with Terraform
Build a production observability stack on AWS with Terraform
Requirements
Basic familiarity with Grafana: logging in, viewing dashboards, and adding datasources
Grafana Cloud Account (free tier)
Comfort with the command line: files, environment variables, and running scripts in Bash
Basic Docker: run containers and map ports
Basic Kubernetes: pods, Deployments, Services, and reading logs with `kubectl`
Basic git and other installed tools: `terraform`, `kubectl`, and the `aws` CLI.
Description
I have taught ~17000 students about monitoring with Prometheus and Grafana. Recently, I’ve been receiving several questions about how to deploy Grafana MCP in production. This course contains an extensive library of information to help you get up and running with MCP from the ground up. You will gain practical experience that you can immediately apply in your production systems.
At the end of the course, you’ll have experience with the following
Running Grafana MCP locally with Docker and connecting it to a real Grafana instance.
Connecting Grafana MCP to Grafana Cloud and query Cloud-backed metrics, logs, and dashboards.
Deploying Prometheus, Loki, Grafana, and a demo app on AWS with Terraform.
Deploying Grafana MCP on Amazon ECS Fargate and Amazon EKS with TLS, IAM, and Secrets Manager.
Querying live dashboards, metrics, logs, and alerts from Cursor, Claude Code, Devin Desktop, and OpenCode.
Explaining MCP architecture: clients, servers, tools, and transports (stdio, SSE, and streamable HTTP).
Scoping Grafana service accounts and MCP tools so the assistant runs with least privilege, not admin.
Identifying MCP security risks and apply the controls you actually need on AWS.
Choosing an ECS or EKS architecture for Grafana MCP and monitoring the MCP server itself.
Investigating incidents from your editor using live Prometheus and Loki data instead of screenshots.
Who this course is for
Platform and DevOps engineers who need to run and manage MCP servers in production
Developers who use Grafana daily and want production context in their AI assistant
SREs or oncall engineers who want faster incident triage while adhering to least-privilege principles
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